Implementation of Forward Chaining and Certainty Factor Method on Android-Based Expert System of Tomato Diseases Identification

An expert system is a system that adopts the expertise of an expert in a particular field into the system. Rabbits are one of the animals that are close to humans and have high body adaptability. However, rabbits are not free from the threat of disease. Treatment requires more costs so we need a way to find out the disease and the solution so that we can take the necessary actions. To overcome this, an expertise media was created that can be accessed by rabbit owners online using the Forward Chaining and Certainty Factor methods. The expert system must be able to work under uncertainty, so the Forward Chaining (FC) and Certainty Factor (CF) methods were added to overcome this problem. Forward Chaining is a decision making method that is commonly used in expert systems and the Certainty factor method is a method to prove the uncertainty of an expert's thinking. The system diagnosis results display possible diseases suffered by rabbits and provide treatment solutions for diseases with the largest CF values. With the calculation results, the level of confidence information based on the table is Definitely Yes. The accuracy of the system output is proven from the results of system validity with experts.

Tuberculosis (TB) is an infectious disease that can affect people of all ages, including children, adolescents, and the elderly, and can cause illness and death in over one million people. The disease is spread through coughs or sneezes by people with pulmonary TB, through contaminated saliva, and inhalation by healthy people with weakened immune systems. Therefore, this study aims to develop an expert system to assist in the diagnosis of pulmonary tuberculosis using the Forward Chaining and Certainty Factor methods. This process begins by identifying symptoms reported by the user and then searching for rules in the knowledge base that match those symptoms. This method allows the system to follow a logical flow of reasoning similar to the way a doctor diagnoses a disease. This study used data from 100 patients from 2023 at the Pariaman Community Health Center. Using the Forward Chaining and Certainty Factor methods, three patient data sets with three types of tuberculosis were tested. The percentage results for each type of disease were 100% positive for pulmonary tuberculosis, 0.91% negative for pulmonary tuberculosis, and 0.92% latent for pulmonary tuberculosis, with a confidence level of Very Confident. This research contributes to increasing knowledge and understanding in the field of expert systems, particularly in the application of the Forward Chaining and Certainty Factor methods for diagnosing tuberculosis.

Hydroponics systems require tremendous attention both in nutrition, plant growth, the risk of pest and disease attacks. Thus, this hydroponic farming system requires high experience and expertise that is the main obstacle for business actors who want to start a vegetable cultivation business with a hydroponic system, especially for business actors who do not have a background in agriculture. The hydroponic cultivation expert system in this study aims to detect plant diseases and pests and provide solutions to these diseases. In addition, this expert system application also aims to monitor plant needs, so this application can help farmers grow up hydroponic vegetable cultivation businesses. The combination of the forward and backward chaining methods is applied to detect plant diseases and pests. Whereas the forward chaining method use to monitor plant needs. The combination of the forward and backward chaining methods in identifying plant diseases and pests can make it easier for users to determine the suffered by plants and provide information about these diseases. The application of the forward chaining method in monitoring plant needs can identify the level of fulfilment of entails of each component of plant needs according to the type of plant and the day after planting. The application of this hydroponic expert system can overcome the limited number of hydroponic experts in helping farmers identify plant diseases and pests and monitor plant needs.

Kidney disease is a prevalent health issue affecting millions of people globally. Early and accurate diagnosis of kidney diseases can help in the timely and effective management of the condition. Expert systems, such as those using the Certainty Factor (CF) method, can provide doctors with valuable assistance in diagnosing kidney diseases more efficiently and accurately. This study aims to develop a kidney disease diagnosis expert system using the CF method. The developed system consists of data collection, data storage, and data processing components, with the CF method used to calculate diagnostic confidence levels and decision-making based on predetermined rules. The knowledge acquisition process was carried out by interviewing three nephrologists to obtain rules for diagnosing kidney diseases. The expert system's evaluation is conducted by comparing the system's diagnostic accuracy with a specialist doctors. The results show that the developed expert system has an accuracy rate of 85.7% in diagnosing kidney diseases. The system also has a user-friendly interface, which allows doctors to input symptoms and obtain a diagnosis quickly and accurately. The developed system has several advantages over traditional diagnosis methods. It can diagnose multiple kidney diseases simultaneously and provide a differential diagnosis, allowing doctors to choose the most appropriate treatment plan for their patients. The system also has the potential to reduce diagnostic errors and improve patient outcomes.

Covid-19 is a new type of virus that is currently a pandemic in almost all countries. The limitations of experts for diagnosis and recommending rapid action to patients who are diagnosed positively, as well as the limitations of the expert area in the environment wh ere the Covid-19 outbreak occurred. This expert system aims as an early diagnosis system for Covid-19 so that patients can receive treatment immediately, and help stop the wider spread of the virus. Using predetermined parameters and there is also a system that can later make it easier for users to find out the diagnosis of Covid-19 caused by the coronavirus. The method used for this expert system research is Certainty Factor (CF). This method aims to showed how much the confidence value is based on the clinical parameters given by MYCI. The advantage of the Certainty Factor method is that it can be measured something that is certain or uncertain. And to maintain accuracy, Certainty Factor can only process two data. The Expert System uses the Certainty Factor (CF) method to diagnose Covid-19 disease, starting with the collection of symptom data and giving confidence values by the expert on each symptom, then the symptom data input stage from the user and the system will calculate based on the Certainty Factor rule, multiplying the weight value namely user CF and expert CF, combining the results of the multiplication of each symptom, so that a percentage of confidence is obtained as the final diagnosis result. After calculating the accuracy test of the 3 rule system with 99% positive Covid-19 results, 48% most likely Covid-19 and 29% negative Covid-19.

The management of complex neurological diseases such as Multiple Sclerosis (MS) requires accurate and efficient diagnostic approaches. To enhance diagnostic precision, a study has conducted a comparison between two approaches within the framework of an expert system, namely the Case-Based Reasoning (CBR) Method and the Certainty Factor (CF) Method. The primary objective of this study is to evaluate the effectiveness of these two methods in supporting the diagnosis process of Multiple Sclerosis. The Case-Based Reasoning Method is an approach that relies on past experiences to address new issues. Within an expert system, CBR utilizes knowledge from previous cases to identify diagnoses that align with the current situation. On the other hand, the Certainty Factor Method is an approach that measures the confidence level in a statement based on rules and associated confidence factors. This study makes use of a dataset containing information from previous cases related to the diagnosis of Multiple Sclerosis. By employing both of these methods, an expert system is developed to provide diagnostic recommendations based on inputted symptoms and data. The effectiveness of both approaches is evaluated through diagnostic accuracy, computational speed, and confidence levels in the generated results. Research findings indicate that both methods have their respective strengths and weaknesses. The CBR method tends to yield accurate results by referring to similar cases in the past, but it may encounter challenges in unique or rare cases. On the other hand, the Certainty Factor Method has the ability to handle uncertainty and can produce results with measurable confidence levels. However, dependence on predefined rules may limit adaptation to new cases. In conclusion, this study underscores that there is no singular perfect approach within expert systems for diagnosing Multiple Sclerosis. Both the CBR and Certainty Factor methods contribute in their own ways to improving accuracy and confidence in the diagnosis process. Therefore, integrating these two methods could be a promising direction for the development of expert systems in the future.

Student health directly affects the quality of learning and academic achievement. When sleep patterns and daily routines become irregular, students face a higher probability of developing health-related problems. To address this issue, a web-based expert system was designed and developed to detect student health risks based on sleep patterns and daily activities, applying the Certainty Factor (CF) method, with a case study conducted at STIE BPKP. The Certainty Factor method was used to quantify the degree of confidence in a particular health risk by combining symptom data selected by users with certainty weights assigned by domain experts. The system was built as a web application featuring three core functions: symptom data input, diagnostic processing, and output presentation in the form of health risk levels along with actionable recommendations. Functional testing was carried out to verify that each module operated correctly and that diagnostic outputs aligned with the expected Certainty Factor calculations. Findings from this study confirm that the expert system successfully assists students in assessing their health risk levels in a fast and accessible manner. Beyond individual use, the system also serves as a supporting instrument for academic institutions seeking to strengthen health awareness programs among their student population. As a result, this expert system holds practical value as an early-warning tool for student health risk detection.

Depression is a growing mental health concern among university students, often fueled by academic pressure, social demands, and personal stress. This study presents the development of an expert system using the Certainty Factor (CF) method to diagnose depression specifically among students at Ngudi Waluyo University. The system categorizes depression into mild, moderate, and severe levels based on 12 validated symptom statements and expert-defined diagnostic rules. Implemented with PHP, JavaScript, and CSS, the system offers a user-friendly, accessible, and anonymous platform for self-assessment. Testing yielded an accuracy rate of up to 79% in diagnosing depression severity and a 71.7% user satisfaction rate based on a User Acceptance Test (UAT) involving 32 students. Results demonstrate that the system can effectively support early detection and mental health awareness within academic environments. Despite some limitations in UI and feedback depth, the expert system shows strong potential for broader application and further enhancement.

Indonesia is in the area of equatorial latitude which is known as a tropical climate country. On the other hand, many Indonesian people are prone to suffering typical tropical disease such as typhoid, dengue and malaria. With the motivation to curb disease, they should be informed about awareness, treatment and knowledge regarding to tropical diseases such as the symptoms, causes and early prevention. Web-based expert system is one well-known solution, which can access online via internet. Usually, traditional inference engine is possible to make misdiagnosis in medical domain. In addition, modern inference e.g. Bayes theory are complicated plus insufficient to substitute complete human brain reasoning activities. Therefore, this study aims to hybridize Forward Chaining and Certainty Factor method for diagnosing common tropical diseases suffered by Indonesian people. We have used ten types of tropical disease namely typhoid, dengue, chingkungunya fever, malaria, chicken pox, tuberculosis, diphtheria, pertussis, SARS and elephantiasis along with 38 symptoms for representing every disease into a knowledge base. We compare the results between expert's diagnosis and our proposed web-based expert system after running ten times consecutively. We can conclude that hybridization of Forward Chaining and Certainty Factor methods during developing the web-based expert system, can significantly diagnose tropical diseases properly.

The aglaonema plant is quite in demand by the public because it is a current trend and can be a business field for ornamental plant lovers. However, the growth of aglaonema plants can be hampered by disease, so a system is needed. This Aglaonema plant disease diagnosis expert system was created to determine the type of disease so that it can be overcome as early as possible. The expert system method used is the Forward Chaining method and the Naive Bayes method. The Forward Chaining method is used to make rules based on symptoms and the Naïve Bayes method to determine the results of the diagnosis. This study used 25 symptoms for 10 diseases and obtained an accuracy value of 76% using 50 data.

Lack of blood or anemia is a condition when the body lacks healthy red blood cells or when red blood cells do not function properly. As a result, the organs of the body do not get enough oxygen, making people with anemia pale and tired easily. Anemia is often ignored because it is considered just an ordinary disease. But if left untreated, this disease can be dangerous. So that people are expected to also have knowledge about this disease and its symptoms and how to handle and prevent it so that the risk can be reduced. An expert system is a form of utilizing computer technology that adopts human knowledge to computers, so that computers can solve problems as is usually done by experts. An expert system is a computer system that is able to imitate the reasoning of an expert with expertise in a particular knowledge. An expert system can replace the role of an expert by utilizing a computer which in the end can provide a definite solution as done by an expert. The expert system for diagnosing anemia is built using the certainty factor method which can provide results in the form of the possibility of anemia suffered, the percentage of belief, symptoms, and treatment. The results of this study indicate that the expert system that was built can determine the value of the degree of confidence or the certainty factor of the data obtained from the results of consultations with patients through the certainty factor method.

Disorders of the eye become a disease that is often experienced. There is a new symptom model found, causing confusion that occurs. Diseases of the eye often attack humans, especially in the current era of digital technology, because they spend more time staring at cell phone or computer screens. So the application of this expert system is made to provide solutions for the community to diagnose the symptoms that are often experienced. In making this application, the forward chaining method and certainty factor are used in the application built and comparison with the combined forward chaining and naive Bayes method. The test is carried out by testing 40 data samples based on existing symptoms, carried out by the system and also manually to see the accuracy of the values given. The results given are that there is a fairly high level of certainty in the diagnosis of hypermetropia with a certainty value of 88.43% and the smallest certainty value in the diagnosis of presbyopia with a value of 61.702% of the total testing obtained a probability level of certainty ranging from 51%-79%.

The skin is the outer protective organ for humans. Skin is the most interacting layer with the environment. Interactions that occur are not always safe against bacteria, viruses, fungi and parasites, so they will cause harm. Poor interactions will result in skin diseases. This skin disease is often considered normal, but can be dangerous and deadly and contagious. Due to the natural conditions and lack of medical personnel, sufferers of skin diseases have problems in examining their skin diseases. So this research was conducted by using an Expert System (Expert System) to help sufferers of skin diseases and help get solutions to these diseases. The solution obtained from the symptoms felt by sufferers. The method used in this study is Forward Chaining (FC) and processed using the Certainty Factor (CF) Method. The results of this study can determine the right initial steps in dealing with infectious skin diseases. So this research is very helpful for sufferers in knowing the type of skin disease, the solution in its prevention and its prevention precisely.

Farmers still do not really understand how to diagnose problems in plants, one example is strawberry farmers. This is proven by several farmers and strawberry plant cultivators, not all of whom can understand the disease where there are various types of diseases that can attack strawberry plants with almost the same symptoms , and if a farmer or cultivator is wrong in handling the type of strawberry plant disease, it is not impossible that it will cause the strawberry plant to die. Strawberry farmers need a tool to diagnose strawberry plant diseases so that they can find out the condition of the strawberry plants. Therefore, an expert system was created to diagnose diseases in strawberry plants and find solutions to deal with the damage that occurs. The system built for diagnosing diseases in strawberry plants uses the Forward Chaining method. Forward chaining is a forward tracking that starts from a set of facts by looking for rules that match the existing hypothesis towards a conclusion. In its implementation, this system has met these objectives by using a database and rule base. The system draws conclusions based on existing facts using the forward chaining method, the search starts from the facts from which new conclusions are obtained, the existing rules are traced one by one until the search is stopped because the last condition has been met.

The human lungs are organs that are susceptible to disease. The high risk of death of lung disease patients shows that this type of disease needs to be taken seriously. This is due to a lack of public knowledge and awareness about lung health. The immense number of lung disease sufferers is inversely proportional to the availability of pulmonogists, in Pontianak City alone in 2016 only had 3 pulmonologists.To overcome these problems, in this study an expert system was built using the Certainty Factor method. The process of calculating Certainty Factor uses the trust value given by experts and also the user to produce a diagnosis of the disease, each symptom has an MB and MD value for a disease given by an expert to determine the results of a system diagnosis based on symptoms felt by the user. The CF method has a system performance that is able to run according to functional requirements and results in a high accuracy percentage.The expert system that has been built in this study is based on Android, by calculation based on the results of testing, all expert system functionality in diagnosing lung disease goes according to system requirements, and has an accuracy rate of 85.18%. of 27 test data.Keywords: Lung Disease, Expert System, Certainty Factor, Android.